In these exercises we will see the power of the libraries ggplot2 and plotly to make sense of statistical data. The goal is to reproduce the moving chart that you can see in this video from Hans Rosling – I invite you to watch his other videos, they are quite enlightening and inspiring:



For this, we will need to gather the data:

1 Data handling

The first thing to do is to load and regroup all these datasets into a single one.

  1. Load the tidyverse library and, using read_csv(), load the 4 datasets in 4 separate tibbles called children, income, pop and religion.

  2. To reproduce the chart on the video, we need to determine the dominant religion in each country. In the religion dataset, add a column Religion that will give the name of the dominant religion for each country. For this, you might want to use this method that returns the name of the column containing the maximum of each row of a table:

DF <- tibble(V1=c(2,8,1),V2=c(7,3,5),V3=c(9,6,4))
DF
## # A tibble: 3 × 3
##      V1    V2    V3
##   <dbl> <dbl> <dbl>
## 1     2     7     9
## 2     8     3     6
## 3     1     5     4
names(DF)[max.col(DF)]
## [1] "V3" "V1" "V2"
  1. Using pivot_longer(), make all datasets tidy.
  • children should now contain 3 columns: Country, Year and Fertility.
  • income should now contain 3 columns: Country, Year and Income.
  • pop should now contain 3 columns: Country, Year and Population.

We will only consider data from 1800 to 2018. Example of syntax using the pipe operator %>%:

DF <- read_table("name  2010  2011  2012  2014
Kevin  10    11   12   123
Jane   122   56   23   4
"
)
DF
## # A tibble: 2 × 5
##   name  `2010` `2011` `2012` `2014`
##   <chr>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 Kevin     10     11     12    123
## 2 Jane     122     56     23      4
DF %>% 
    select(name, '2010':'2012') %>% 
    pivot_longer(col=-name,
                 names_to="Year", 
                 values_to="Score",
                 names_transform=list(Year = as.numeric))
## # A tibble: 6 × 3
##   name   Year Score
##   <chr> <dbl> <dbl>
## 1 Kevin  2010    10
## 2 Kevin  2011    11
## 3 Kevin  2012    12
## 4 Jane   2010   122
## 5 Jane   2011    56
## 6 Jane   2012    23

The line names_transform=list(Year = as.numeric) is here to convert the character year values to numerical values.

  1. Now we want to combine all these datasets into a single one called dat, containing the columns Country, Year, Population, Religion, Fertility and Income. Look into the inner_join() function of the dplyr library (which is part of the tidyverse library). For the religion dataset, we will consider that the proportions of 2010 are representative of all times.
  • So play with the religion dataset so that it contains only two columns, Country and Religion, the data being filtered from the original religion dataset for the year 2010.
  • Join the 4 datasets into one called dat.

You should end up with a dataset like this one:

## # A tibble: 37,887 × 6
##    Country      Year Fertility Income Population Religion
##    <chr>       <dbl>     <dbl>  <dbl>      <dbl> <chr>   
##  1 Afghanistan  1800         7    603    3280000 Muslims 
##  2 Afghanistan  1801         7    603    3280000 Muslims 
##  3 Afghanistan  1802         7    603    3280000 Muslims 
##  4 Afghanistan  1803         7    603    3280000 Muslims 
##  5 Afghanistan  1804         7    603    3280000 Muslims 
##  6 Afghanistan  1805         7    603    3280000 Muslims 
##  7 Afghanistan  1806         7    603    3280000 Muslims 
##  8 Afghanistan  1807         7    603    3280000 Muslims 
##  9 Afghanistan  1808         7    603    3280000 Muslims 
## 10 Afghanistan  1809         7    603    3280000 Muslims 
## # … with 37,877 more rows

In case you struggled to get there, download the archive with the button at the top and get the dat tibble with dat <- read_csv("Data/dat.csv").

Now our dataset is ready, let’s plot it.

2 Plotting

  1. Load the library ggplot2 and set the global theme to theme_bw() using theme_set()

  2. Create a subset of dat concerning your origin country. For me it will be dat_france

  3. Plot the evolution of the income per capita and the number of children per woman as a function of the years, and make it look like that (notice the kinks during the two world wars):

  1. Create a subset of dat containing the data for your country plus all the neighbor countries (if you come from an island, the nearest countries…). For me, dat_france_region will contain data from France, Spain, Italy, Switzerland, Germany, Luxembourg and Belgium.

  2. Plot again income and fertility as a function of the years, but add a color corresponding to the country and a point size to its population:

  1. Load the library plotly and make the previous graphs interactive. You can make an interactive graph by calling ggplotly(), like that:
library(plotly)
P <- ggplot(data = dat_france, aes(x=Population, y=Income))+
        geom_point()
ggplotly(P)# add dynamicTicks=TRUE allows redrawing ticks when zooming in
  1. Finally, you can add a slider to the interactive graph allowing selecting a value for another variable (just like in the video) by adding the keyword frame = in the chart’s aesthetics. So now, make the graph of the video ! (you can also add the aesthetics id=Country to show the country name in the popup when hovering on a point).